NAME

       sccan - part of ANTS registration suite

DESCRIPTION

   COMMAND:
              sccan

              A  tool  for  sparse  statistical  analysis on images : scca, pscca (with options),
              mscca. Can also convert an imagelist/mask pair to a binary matrix image.

   OPTIONS:
       -h

              Print the help menu (short version).

       --help

              Print the help menu (long version).  <VALUES>: 1

       -o, --output outputImage

              Output dependent on which option is called.

       -p, --n_permutations 500

              Number of permutations to use in scca.

       -s, --smoother 0

              Smoothing function for variates

       -z, --row_sparseness 0

              Row sparseness - if (+) then keep values (+) otherwise allow +/- values ---  always
              L1

       -i, --iterations 20

              Max iterations for scca optimization (min 20).

       -n, --n_eigenvectors 2

              Number of eigenvectors to compute in scca/spca.

       -r, --robustify 0

              rank-based scca

       -c, --covering 0

              try to make the decomposition cover the whole domain, if possible

       -g, --uselong 0

              use longitudinal formulation ( > 0 ) or not ( <= 0 )

       -l, --l1 0

              use l1 ( > 0 ) or l0 ( < 0 ) penalty, also sets gradient step size e.g. -l 0.5 ( L1
              ) , -l -0.5 (L0) will set 0.5 grad descent step for either penalty

       --PClusterThresh 1

              cluster threshold on view P

       --QClusterThresh 1

              cluster threshold on view Q

       -e, --ridge_cca 0

              Ridge cca.

       --initialization NA

              Initialization file list for Eigenanatomy - must also pass mask option

       --initialization2 NA

              Initialization file list for SCCAN-Eigenanatomy - must also pass mask option

       --mask NA

              Mask file for Eigenanatomy initialization

       --mask2 NA

              Mask file for Eigenanatomy initialization 2

       --partial-scca-option PminusRQ

              Choices for pscca: PQ, PminusRQ, PQminusR, PminusRQminusR

       --prior-weight 0.0

              Scalar value weight on prior between 0 (prior is weak) and  1  (prior  is  strong).
              Only engaged if initialization is used.

       --get-small 0.0

              Find smallest eigenvectors

       -v, --verbose 0

              set whether output is verbose

       --imageset-to-matrix [list.txt,mask.nii.gz]

              takes  a  list of image files names (one per line) and converts it to a 2D matrix /
              image in binary or csv format depending on the filetype used to define the output.

       --timeseriesimage-to-matrix                      [four_d_image.nii.gz,three_d_mask.nii.gz,
       optional-spatial-smoothing-param-in-spacing-units-default-zero,
       optional-temporal-smoothing-param-in-time-series-units-default-zero
              ]

              takes a timeseries (4D) image and  converts  it  to  a  2D  matrix  csv  format  as
              output.If  the  mask  has  multiple  labels  ( more the one ) then the average time
              series in each label will be computed and put in the csv.

       --vector-to-image [vector.csv,three_d_mask.nii.gz, which-row-or-col ]

              converts the 1st column vector in a csv file back to an image ---  currently  needs
              the  csv  file to have > 1 columns. if the number of entries in the column does not
              equal the number of entries in the mask but the  number  of  rows  does  equal  the
              number of entries in the mask, then it will convert the row vector to an image.

       --imageset-to-projections            [list_projections.txt,list_images.txt,           bool
              do-average-not-real-projection ]

              takes a list of image and projection files names (one per line) and writes them  to
              a csv file --- basically computing X*Y (matrices).

       --scca two-view[matrix-view1.mhd,matrix-view2.mhd,mask1,mask2,FracNonZero1,FracNonZero2]

              three-view[matrix-view1.mhd,matrix-view2.mhd,matrix-view3.mhd,mask1,mask2,mask3,FracNonZero1,FracNonZero2,FracNonZero3]
              partial[matrix-view1.mhd,matrix-view2.mhd,matrix-view3.mhd,mask1,mask2,mask3,FracNonZero1,FracNonZero2,FracNonZero3]
              dynsccan[matrix-view1.mhd,matrix-view2.mhd,mask1,mask2,FracNonZero1,FracNonZero2]

              Matrix-based  scca  operations  for  2  and  3  views.For  all  these  options, the
              FracNonZero terms set the fraction of variables to use in the estimate. E.g. if one
              sets  0.5  then half of the variables will have non-zero values. If the FracNonZero
              is (+) then the weight vectors must be positive. If they are negative, weights  can
              be  (+)  or (-). partial does partial scca for 2 views while partialing out the 3rd
              view.

       --svd sparse[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix] --- will only use  view1
              ... unless nuisance matrix is specified.

              classic[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix]   ---  will  only  use
              view1       ...       unless       nuisance       matrix       is        specified.
              cgspca[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix] --- will only use view1
              ... unless  nuisance  matrix  is  specified,  -i  controls  the  number  of  sparse
              approximations  per  eigenvector,  -n  controls  the number of eigenvectors.  total
              output will then be  i*n sparse eigenvectors.  prior[ matrix.mha  ,  mask.nii.gz  ,
              PriorList.txt , PriorScale.csv , PriorWeightIn0to1 , sparseness ] ... if sparseness
              is    set    to    zero,    we     take     sparseness     from     the     priors.
              network[matrix-view1.mhd,mask1,FracNonZero1,guidance-matrix]
              lasso[matrix-view1.mhd,mask1,Lambda,guidance-matrix]
              recon[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix]
              recon4d[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix]

              a sparse svd  implementation  ---  will  report  correlation  of  eigenvector  with
              original data columns averaged over columns with non-zero weights.